Infrastructure Software Engineer

About Normal Computing

Normal Computing builds silicon that turns thermal noise from an obstacle into a computational resource. Conventional chips spend most of their energy forcing determinism onto physics; ours compute with it. Stochastic, in-memory, asynchronous: the result is 10-100× more AI inference per dollar, per watt.

We co-design the full stack: AI-native EDA systems in production with the world's largest semiconductor companies, and the advanced ASICs they make possible. Backed by $85M+ from the world's leading deep-tech investors and built by scientists, engineers, and operators from the labs that built modern computing.

Normal works as one team across New York, Silicon Valley, London, Copenhagen, and Seoul. We hire people who want the hardest version of their craft, across every discipline, at every seniority.

The Role

As an Infrastructure Software Engineer at Normal, you will build the production systems behind Normal's AI products. This is an application engineering role focused on infrastructure-shaped software: orchestration services, execution runtimes, internal APIs, persistence layers, observability, and developer experience. You'll help define the runtime layer for a new class of AI products: systems where agents execute long-running work, coordinate across distributed environments, interact with code and tools, and need to be reliable enough for real customer workflows.

This role sits between product engineering, AI engineering, and platform engineering. You will not primarily be managing Terraform, Helm charts, CI/CD, or company-wide SaaS infrastructure. Instead, you'll own the application-level infrastructure that powers long-running AI workflows: session lifecycle, sandboxed execution, workload orchestration, persistence, observability, reliability, and the internal interfaces other engineers build on. Developer experience matters: APIs should be understandable, failure modes should be debuggable, and abstractions should make the right thing easy.

On any given day, you might design the runtime architecture for a new AI product capability, build the orchestration layer for long-running autonomous workflows, improve how workloads are scheduled and isolated across distributed environments, or create the systems abstractions that let engineers turn ambitious AI prototypes into reliable production products.

What You'll Own

• Runtime & Orchestration: Build and maintain production software infrastructure for Normal's AI products, especially orchestration, execution, and runtime systems.
• Internal APIs: Design internal backend services and APIs used by product engineers, AI engineers, execution services, and other internal systems.
• Operational Maturity: Improve rapidly evolving systems through better state management, failure handling, metrics, tracing, and debugging tools.
• Execution Environments: Work with Kubernetes-backed execution environments, including container lifecycle, scheduling behavior, autoscaling, resource isolation, and runtime reliability.
• Developer Experience: Build developer-facing tools and abstractions that make it easier for other engineers to use and extend the systems you own.
• Prototype-to-Production: Turn promising prototypes into durable production systems by designing clear abstractions, hardening critical paths, and creating operational patterns that scale with the product.
• Design Leadership: Lead design discussions for core runtime and orchestration systems, including API boundaries, state management, execution models, and operational tradeoffs.

What Makes You a Great Fit

• 4+ years of experience in infrastructure software, backend infrastructure, production infrastructure, platform engineering, distributed systems, or related areas
• Strong software engineering fundamentals, including backend programming, APIs, data modeling, concurrency, debugging, and testing
• Experience building or operating production services where reliability, observability, and maintainability matter
• Practical experience with Docker and Kubernetes, including debugging containerized workloads, deployments, networking, resource limits, and lifecycle issues
• Comfort working with persistence systems such as Postgres, Redis/Valkey, object storage, or similar production data stores
• Experience building orchestration systems, job schedulers, workflow engines, sandboxes, developer platforms, or distributed execution systems
• Strong systems thinking: you can reason about state machines, failure modes, retries, queues, leases, scheduling, and long-running workflows
• Pragmatism in fast-moving environments: you know when to improve an abstraction, when to delete one, and when to ship the simple version
• Ownership mindset: you care whether the systems you build work in production and are usable by other engineers

Bonus Points

• Deep Kubernetes experience, such as controllers/operators, networking, storage, scheduling, autoscaling, or resource isolation
• Experience with AI agent infrastructure, ML infrastructure, model orchestration, or LLM-based product systems
• Background in production infrastructure, reliability engineering, or infrastructure software at meaningful scale
• Experience in high-growth startups or engineering teams where ownership boundaries are still being defined
• Experience with chips, EDA, or design verification

Equal Employment Opportunity Statement

Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status.

Accessibility Accommodations

Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at View email address on click.appcast.io.

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